Green architecture has gained increasing attention as an environmentally responsible approach that supports human well-being, sustainable lifestyles, and improved living environments. Although many studies have examined green building principles, the direct relationship between green architecture and quality of life remains insufficiently integrated, as much of the existing literature focuses mainly on technical and environmental performance. This systematic review addresses this gap by identifying and analysing the key indicators of green architecture that contribute to quality of life and by proposing a conceptual framework linking sustainable design with human well-being. Relevant academic literature was systematically reviewed to extract, classify, and compare the most frequently discussed indicators. The findings show that social well-being was the most frequently identified indicator, appearing in 88% of the reviewed studies, followed by community impact and resource management at 84%, and pollution reduction and ecological footprint at 80%. These results indicate that green architecture should be understood not only as a technical response to environmental challenges but also as a holistic strategy for creating healthier, more livable, and socially responsive environments. The study highlights the interrelationships among environmental, social, and resource-based indicators and provides practical guidance for architects, urban developers, and decision-makers seeking to integrate sustainable and human-centred principles into contemporary built environments.
The maturity technique offers a practical approach for forecasting early-age strength development. This study aimed to evaluate the accuracy of Giatec's SmartRock sensors by comparing their measurements with conventional specimen test results derived from the maturity index. Twelve concrete combinations were evaluated, with compressive strengths ranging from 20 to 45 MPa. The nine concrete mixes were designed with varying proportions of cement, sand, coarse aggregate, water, and admixture, as summarised in Table 1. The cement content ranged from 290 to 430 kg/m³, water-to-cement ratios varied from 0.27 to 0.51, and admixture dosages ranged from 0.5% to 1.2% by weight of cement, depending on the desired strength level (20-63 MPa). Cylinder tests performed at 1, 3, 7, 14, and 28 days provided the data necessary to calibrate the maturity–strength relationships. The resulting correlations between compressive strength and maturity index showed excellent accuracy, with coefficients of determination (R²) ranging from 0.986 to 0.999 across all twelve mixes (20-45 MPa). These results confirm that the SmartRock maturity method reliably predicts early-age strength and offers real-time assessment of in-situ concrete performance. The best-fit Vipulanandan models yielded coefficients of determination (R²) ranging from 0.986 to 0.999, demonstrating excellent agreement between sensor-predicted and laboratory-measured strengths. Predicted early-age strengths (3–7 days) differed from test values by less than 5%, confirming the reliability of the SmartRock system for field applications. The study concludes that the maturity method, when properly calibrated, provides a robust and non-destructive means to predict in-place concrete strength in real time, enabling faster formwork removal and improved construction quality control.
The transition toward industrialized housing construction involves shifting from traditional, craft-based practices to standardized, process-oriented, and technology-driven production systems. This study examines the extent to which such a transition is occurring in Jordan’s residential construction sector by assessing the presence of key building industry characteristics and identifying major implementation gaps. A structured questionnaire, based on Lessing’s industrialized construction framework, was used to evaluate eight core characteristics: process planning and control, off-site prefabrication, advanced technical systems and automation, information and communication technology (ICT), stakeholder relationships, supply chain integration, customer focus, and performance measurement. Data were collected from 203 construction professionals representing a wide range of residential sector stakeholders. The results indicate a partial and uneven adoption of industrialized construction practices. While planning, basic ICT usage, and performance measurement show moderate levels of implementation, critical features such as off-site prefabrication, automation, standardized systems, and long-term stakeholder collaboration remain weakly developed. Significant variability among firms highlights a fragmented production environment characterized by reactive logistics and limited supply chain integration. Overall, the findings suggest that Jordan’s housing sector exhibits a semi-industrialized profile, underscoring the need for coordinated policy support, capacity building, and enhanced stakeholder collaboration to advance industrialized housing construction.
Rapid urbanisation in mountainous cities has accelerated the tension between the needs of development and conservation of natural identity. The research emphasises the importance of natural features (mountains) and their visibility in a city, which are usually cultural and natural landmarks that preserve the city's original natural identity. It examines how high-rise housing complexes impact the city's natural identity and visual scenery. The primary objective of this study is to evaluate the visual impact of residential building design on natural mountain landscapes (identity) using geographical information system (GIS) viewshed analysis. The results have shown that (88%), that's about (321.81 Ha), the natural context of the Goizha Mountain is interrupted and blocked by high-rise buildings. And only (12%), that is about (45.73 Ha), of the mountain scenery is visible. The study confirms that the disintegration of architectural design (form) from its natural context leads to significant visual distortion, ecological imbalance, a weakened sense of place, and the erosion of the city's natural identity. It highlights that context-sensitive design and height regulation are necessary to preserve the natural identity in mountainous areas.
Students with physical disabilities often encounter significant mobility and accessibility challenges in hybrid learning environments, particularly in dynamic classroom settings where conventional wheelchairs lack adaptability and compatibility with hybrid classroom layouts and digital learning activities. This research developed and evaluated a smart wheelchair equipped with sensor-based automated navigation to enhance mobility, independence, and accessibility in hybrid classrooms. The research employed a Research and Development approach using the ADDIE model, integrating prototype development, functional testing, mechanical simulation, and expert validation. The system incorporated a joystick and smartphone-based control, ultrasonic and line sensors for obstacle detection, GPS-assisted positioning, and an auto-parking feature. Mechanical simulation results indicated structural safety with a high safety factor (7.47) and minimal deformation (0.0429 mm). Performance testing demonstrated stable navigation, responsive control with an average latency of 120 ms, and successful auto-parking in 90% of trials. Expert validation involving five specialists showed that the system achieved "highly feasible" ratings across efficiency, durability, aesthetics, precision, and safety indicators. The findings indicate that the proposed smart wheelchair effectively improves independent mobility and supports accessibility in hybrid learning environments. However, further refinement is required to enhance precision and safety performance for broader implementation.
A nanocomposite consisting of a Polyvinylpyrrolidone (PVP) and polyvinyl alcohol (PVA) blend doped with silver nitrate (AgNO₃) was prepared using a combination of chemical reduction and solution casting, with varying silver nanoparticle (Ag-NP) contents. Polymer nanocomposites were prepared by adding AgNO3 (0, 10, 20, and 30 wt.%) to a PVP/PVA host polymeric matrix with a weight ratio containing 60% of (PVP) and 40% of (PVA). The UV-Vis-NIR absorption spectroscopy was employed to examine the optical properties of the prepared films. The impact of various AgNO3 concentrations on the direct and indirect band gap energies was obtained from Tauc's plots, and the refractive index was determined using the optimal formula, as reported in previous studies, which provides an accurate correlation between refractive index and band gap energy. The structural and crystallinity degree were examined by XRD analysis. SEM images showed the nanoparticle formations. The discussion focused on the discovery that the refractive index change increased substantially with increasing salt concentration, consistent with the formation of silver nanoparticles.
This research presents an optimisation framework for determining the optimal location and capacity of photovoltaic distribution generators (PVDGs) in radial distribution networks (RDNs) using two recent algorithms: the Salp Swarm Algorithm (SSA) and the Geometric Mean Optimiser (GMO). Unlike previous studies that treat distributed generation (DG) systems generically, this work offers a detailed modelling approach to PVDG power output and its direct impact on network performance. The framework is tested on both a standard IEEE 33-bus system and a practical Iraqi 66-bus system. Results show significant improvements in network performance after integrating optimal PVDG units, with power losses reduced by 61.334% and 61.571% using SSA and GMO, respectively, in the IEEE 33-bus system, and by 67.660% and 69.781% in the Iraqi 66-bus system. Additionally, the voltage stability index improved by 15.317% and 15.690% in the IEEE system and by 11.568% and 12.689% in the Iraqi system. The GMO algorithm demonstrated superior computational efficiency, converging faster and requiring fewer iterations than SSA. The accuracy and reliability of the results are further validated through comparison with previous work.
Plaster of Paris (POP) is widely utilised in construction and artistic applications, yet its limited mechanical strength and sensitivity to moisture remain significant technical challenges. This study investigates the effects of incorporating nanosilica, glass fibres, and polyvinyl alcohol (PVA) on the performance of POP-based composites. Various formulations were evaluated for compressive, flexural, and hardness strengths, as well as water absorption. The experimental protocol distinguished between two immersion conditions: a 2 h immersion period was employed specifically for water absorption testing, while mechanical properties (compressive, flexural, and hardness) were evaluated under wet conditions following a 6 h immersion period. Results demonstrate that integrating nano-silica, glass fibres, and PVA increases mechanical resistance and reduces water uptake. Microscopic analysis indicates that nano-silica particles densify the POP matrix through a microfiller effect. These findings suggest that the modified POP composites exhibit improved performance for short-term moisture exposure, although further research is required to assess long-term durability in aqueous environments.
This study presents the development and pilot implementation of a Database-Supported VR/BIM-Based Communication and Simulation (DVBCS) system designed to enhance design comprehension, stakeholder engagement, and decision-making in the early planning stages of a disaster hospital in Jordan. The system integrates database-structured BIM models, a real-time visualisation environment built with Unreal Engine, and a semi-immersive VR projection setup to provide interactive walkthroughs and workflow simulations. A pilot usability evaluation was conducted with 10 participants, including five healthcare professionals and 5 members of the architectural and engineering team. Data were collected through structured questionnaires, observations, and guided discussions during the VR sessions. Results indicate that the DVBCS system improved users' understanding of spatial configurations, facilitated clearer communication, and supported more efficient identification of design issues compared with traditional design communication methods. Participants reported high satisfaction with the system's visual clarity, interactivity, and usefulness for collaborative review. However, the study also identified limitations related to hardware costs, required training, and the small sample size. As a pilot study, these findings are preliminary but demonstrate the potential of VR/BIM-supported approaches to strengthen communication and reduce design errors in healthcare planning. Future research should include larger samples and controlled comparisons to validate the system's effectiveness further.
The study aimed to develop an optimised composition for non-autoclaved aerated concrete using technogenic wastes and to evaluate their Effect on key physical and mechanical properties. Antimony ore beneficiation tailings (AOBT, 10-30%) and basalt fibre waste (BFW, 0-4%) were varied according to a two-factor central composite design. Tests were conducted for density, compressive strength, thermal conductivity, and drying shrinkage in accordance with State All-Union Standard procedures. Density ranged from 612 to 740 kg/m³, with the lowest values at 30% AOBT and 2% BFW, due to enhanced gas formation and reduced solid mass. Compressive strength reached 3.8 MPa in the same composition, reflecting a synergistic filler-fibre effect. Thermal conductivity (0.14-0.23 W/m·°C) decreased with increasing AOBT and optimal fibre content, while shrinkage (0.8-2.8 mm/m) was minimised when both components were increased, indicating spatial stabilisation. Regression analysis showed that AOBT primarily affected density and thermal conductivity, and BFW influenced shrinkage. The mixes are designed to comply with the relevant structural-insulating concrete standards for external load-bearing walls in low-rise construction, specifically meeting the requirements for strength class B3.5 as defined by ASTM C90 and ASTM C495. The optimal mix, which achieves a compressive strength of 2.6 to 3.8 MPa, is suitable for load-bearing walls and meets the minimum strength criteria for structural-insulating applications in low-rise buildings, ensuring both adequate mechanical performance and energy efficiency.
Groundwater serves as the primary source of freshwater in arid and semi-arid rural regions due to low precipitation, limited surface water, and growing socio-economic activities. Understanding the hydrogeochemical processes that influence groundwater quality is essential for effective management and long-term sustainability. This study evaluates the hydrogeochemical characteristics of groundwater within the Dammam Formation in the Samawa region of southern Iraq based on data from 30 wells. Laboratory analyses include measuring pH, EC, TDS, total hardness (TH), major cations (Ca²⁺, Mg²⁺, Na⁺, K⁺), and anions (SO₄²⁻, Cl⁻, NO₃⁻, HCO₃⁻). Groundwater suitability for human consumption was assessed using the Groundwater Quality Index (GWQI) according to World Health Organization (WHO) standards. In contrast, irrigation suitability was evaluated using sodium percentage (%Na), sodium adsorption ratio (SAR), permeability index (PI), magnesium hazard (MH), and Wilcox and USSL classification charts. GWQI results showed that 6.67% of samples were "excellent," 6.67% were "good," 66.67% were "poor," 6.67% were "very poor," and 13.33% were "unfit for drinking." Piper diagram analysis indicated 87% of samples in the Ca-Cl field and 13% in the Ca-Mg-Cl field. Wilcox's results revealed 70% of samples were unsuitable for irrigation and 30% of uncertain use. PI results showed 93.33% in class II, suitable for irrigation, and 63.33% with no magnesium hazard. The study recommends desalination with monitoring of sodium, chloride, and sulfate to improve groundwater quality.
Purple cabbage (Brassica oleracea var. capitata f. rubra) contains anthocyanins that exhibit pH-dependent colour changes and can function as visual indicators of food spoilage through their response to volatile basic compounds. This study developed a simple colourimetric indicator label using purple cabbage extract and evaluated its suitability for monitoring milkfish (Chanos chanos) spoilage under different storage temperatures. Extracts were prepared at three levels (30 g, 50 g, and 70 g of cabbage per 100 mL of solvent) and used to fabricate indicator labels, which were characterised using UV–Vis spectroscopy and FTIR to confirm anthocyanin-related spectral features and functional groups. The labels were tested in sealed 100 mL bottles containing 10 g of milkfish, with the indicator positioned 4 cm above the sample to enable headspace exposure without direct contact. Storage was conducted at 15 °C, 25 °C, and 40 °C. Colour changes were recorded at 2, 10, 24, and 32 h and quantified using image analysis. The indicator labels exhibited progressive colour shifts over time, consistent with the accumulation of volatile bases associated with spoilage. Temperature strongly influenced the response rate, with slower changes at 15 °C and faster, more pronounced changes at 25 °C and 40 °C. These findings indicate that the proposed purple-cabbage-based label has potential as a low-cost, non-destructive tool for monitoring milkfish freshness across practical storage conditions.
The increasing demand for freshwater due to human activities underscores the need for alternative water sources. Desalination of seawater and water reclamation are reliable methods for producing potable water. Among desalination techniques, seawater reverse osmosis (SWRO) is widely adopted due to its lower energy consumption compared to thermal desalination, smaller footprint, reduced waste generation, and consistent performance. Efficient pre-treatment is vital to ensure high-quality feed water and protect RO membranes, which are prone to fouling, scaling, and chemical damage. The HYDRAcapMAX80 ultrafiltration module offers high permeate flux and effectively removes total suspended solids and organic contaminants. A two-pass, two-stage SWRO system was developed, achieving a recovery rate of 45%, salt rejection of 99.1%, and energy consumption of 3.26 kWh/m³. Post-treatment yields permeate water that meets WHO standards, with TDS at 407 ppm, calcium at 6.4 ppm, sodium at 145 ppm, chloride at 204 ppm, fluoride at 0.39 ppm, and a pH of 7.2. System performance was tested under varying salinity and temperature conditions. Higher salinity and lower feedwater temperatures increased TDS and energy consumption, but maintained acceptable performance. This system demonstrates energy-efficient desalination while delivering high-quality drinking water.
Tuberculosis (TB) remains a major global health challenge, demanding rapid and accurate diagnosis to enable timely treatment and reduce disease transmission. Although chest radiography is widely used for TB screening, manual interpretation is often time-consuming, subjective, and constrained by the shortage of experienced radiologists, especially in high-burden, resource-constrained settings. To address these limitations, this study proposes HybridTBNet, a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs) and Transformers for automated TB detection from chest X-ray images. The model is designed to capture both local pathological patterns and global contextual dependencies through hierarchical feature extraction and attention-based representation learning. A curated multi-source dataset of 6,708 high-quality chest X-ray images, comprising 3,194 TB and 3,514 Normal cases after quality filtering, was used for development and evaluation. A strict patient-wise split was applied to prevent data leakage, resulting in separate training, validation, and held-out test sets; however, minor class imbalance persisted in the final test subset. On the unseen test set, HybridTBNet achieved 98.2% accuracy, 97.1% sensitivity, 98.5% specificity, 94.9% F1-score, 0.991 ROC-AUC, and 0.978 PR-AUC, demonstrating strong potential as an effective automated TB screening support tool.
This study presents a methodological approach to elucidate the mechanisms underlying the biosorption of Cu(II) and Pb(II) by live Pseudomonas aeruginosa cells. Experiments were conducted under controlled conditions, and kinetic, equilibrium, and desorption analyses were employed to characterise both transport and attachment mechanisms. The biosorption of Pb(II) was best represented by the pseudo-second-order model, while the fit of Cu(II) was best described by both the pseudo-first- and pseudo-second-order models. The diffusion-chemisorption (D-C) model provided additional insight into transport mechanisms. The solid-phase mass transfer index (RDC) values indicated that intraparticle diffusion played a moderate role in controlling the uptake of both Cu(II) and Pb(II), with a more pronounced effect observed for Pb(II). Equilibrium data conformed best to the Sips isotherm, suggesting heterogeneous surface binding and possible cooperative adsorption. Maximum monolayer capacities were 82.48 mg/g for Cu(II) and 32.50 mg/g for Pb(II). Desorption studies enabled the quantitative attribution of metal binding to distinct interactions, including physisorption, ion exchange, complexation, and intracellular accumulation. Cu(II) biosorption was primarily governed by complexation (51%) and ion exchange (46%), while Pb(II) biosorption was largely driven by ion exchange (64%). The approach formulated in this research offers a practical framework for probing biosorption mechanisms in subsequent investigations.
Drought is a significant natural hazard that poses a significant threat to many countries, including Iraq. It occurs frequently over short and long periods, severely impacting the nation's economic and social sectors. This study presents a comprehensive assessment of drought risk in Iraq by calculating the Drought Hazard Index (DHI) using the Standardized Precipitation Index (SPI) to estimate the probability of meteorological drought. The Drought Vulnerability Index (DVI) was derived from five socio-economic factors: water withdrawal, irrigated land area, population, and agricultural and municipal water use. The Drought Risk Index (DRI) was determined by combining hazard and vulnerability scores. ArcGIS software created spatial maps and applied inverse distance weighting (IDW) for rating calculations. The findings reveal that the Nasiriyah station in the south faces the highest drought risk, while five other stations show moderate risk. Sixteen stations experience low risk, and most northern stations exhibit no drought risk. The elevated risk in the south is linked to limited water resources and overreliance on groundwater. Conversely, low rainfall and limited irrigated farmland have led to lower risk in the north. The study's maps and findings offer valuable tools for guiding drought preparedness and promoting sustainable water resource management in Iraq.
Vehicular Ad Hoc Networks (VANETs) are crucial for modern intelligent transportation systems, enabling real-time communication between vehicles (V2V) and infrastructure (V2I) for applications such as traffic management, accident prevention, and autonomous driving. However, the decentralized and wireless nature of VANETs makes them vulnerable to various cyber threats, including data tampering, eavesdropping, and unauthorized access. Robust security is crucial for protecting sensitive data, maintaining trust, and ensuring the safety of passengers and pedestrians. While existing cryptographic solutions have been proposed, many fail to address the scalability and robustness required for dynamic and resource-constrained VANET environments. This research introduces a novel security framework integrating blockchain technology with SHA3-256 cryptographic hashing. Blockchain provides decentralized trust management, ensuring tamper-proof and verifiable communication, while SHA3-256 enhances data integrity and protects against malicious attacks. A Python-based implementation of this framework demonstrates its scalability and computational efficiency. By securing both V2V and V2I communication, the framework mitigates vulnerabilities, reduces the risk of system malfunctions, and lowers accident rates. This approach establishes a secure and reliable foundation for future intelligent transportation systems, promoting smarter and safer road networks.
Federated Learning (FL) is a decentralized machine learning framework in which clients train models locally and share only their updates with a central server for aggregation. However, FL faces critical communication efficiency and scalability challenges, particularly in large-scale networks where high communication overhead and slow convergence impede deployment. This paper proposes a novel client contribution selection strategy that combines Cosine Similarity and L2 Norm Filtering (CLF-C) with a Normalized L2-Based Client Selection (NLCS) mechanism to enhance communication efficiency and model convergence. The proposed approach prioritizes client updates that are both significant in magnitude and directionally aligned with the global model, thereby reducing unnecessary participation and improving aggregation quality. Experimental evaluations on the MNIST dataset demonstrate that the proposed method achieves 96.8% accuracy in 68 communication rounds using 468 clients, outperforming baseline models such as FedAvg (96.5%, 80 rounds, 800 clients) and FedProx (96.3%, 90 rounds, 900 clients). In addition to reducing communication rounds, the method enhances model robustness by filtering out misaligned or potentially adversarial clients. Importantly, it introduces no additional computational overhead for clients, making it well-suited for resource-constrained environments. This work contributes a scalable and secure solution for improving the efficiency of Federated Learning without compromising model performance.
Due to its larger stability margin, better transient response, and superior robustness to system uncertainties compared to conventional PID controllers, the fractional-order PID (FOPID) controller is gaining increasing attention. This paper proposes a FOPID controller optimized using the Hunger Games Search Algorithm (HGSA) to enhance the performance of an Automatic Voltage Regulator (AVR) system. The FOPID controller introduces two additional parameters, λ and μ, in addition to the conventional proportional (KP), integral (KI), and derivative (KD) gains, thereby offering greater tuning flexibility and smoother control action. HGSA determines the optimal FOPID gains to improve the AVR system's transient response and stability. Key transient performance metrics, such as overshoot, rising time, and settling time, are used to evaluate effectiveness. The performance of the proposed HGSA-optimized FOPID controller is compared with that of other optimization algorithms reported in recent literature. The results demonstrate that the HGSA-based FOPID controller significantly reduces system overshoot by 7.75% to 57.88%, achieves improved rise time, and yields the lowest fitness function value, indicating better convergence characteristics. These outcomes confirm that HGSA is an effective optimization technique for enhancing transient performance, terminal voltage stability, and robustness of AVR systems.
Barren lands can be converted into agricultural land through a multidisciplinary approach to water management. This study evaluates the groundwater quality for irrigation in the uncultivated Faddak land (277 km²) north of Kerbela City, Iraq. Thirty groundwater samples were collected from regional wells and analyzed using GIS, testing, and international standards from the FAO and the Canadian Council of Ministers of the Environment (CCME). A range of physicochemical parameters were tested, including calcium, magnesium, sodium, potassium, sulfate, chloride, total dissolved solids, electrical conductivity, and others. The results showed that most pollutants exceeded permissible limits, with an Irrigation Water Quality Index (IWQI) of 36.28, indicating that the groundwater was unsuitable for irrigation. It is concluded that the water demands of native plants can be met through a combination of rainfall and surface water from the Euphrates River, with a maximum release of 20 m³/s required for cotton cultivation in July if 50% of the area is planted.
